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64 lines
1.9 KiB
64 lines
1.9 KiB
7 years ago
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# Prune
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## Motivation
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We want to support running inference, training and checkpointing in one `ProgramDesc`. We implement
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`void Prune(const ProgramDesc* input, ProgramDesc* output)` function, which takes a `ProgramDesc`
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and generate a pruned `ProgramDesc`.
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## Challenge
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Pruning need to support both variables and operators being evaluation targets. Consider the following
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different situations.
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```python
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# Case 1: run foward pass.
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cost_np = session.run(target=cost)
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# Case 2: run backward passing.
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opts_np, _ = session.run(target=[cost, opt])
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# Case 3: run checkpointing
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_ = session.run(target=checkpoint)
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```
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## Solution
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To support evaluation of operators, we add `is_target` field in the `OpDesc`.
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```c++
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message OpDesc {
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required string type = 3;
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repeated Var inputs = 1;
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repeated Var outputs = 2;
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repeated Attr attrs = 4;
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optional bool is_target = 5 [ default = false ];
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};
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```
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To support evaluation of variables, we add [fetch_op](https://github.com/PaddlePaddle/Paddle/pull/4599).
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For each variable in the `target`, we insert a `fetch_op` into the `ProgramDesc` with `variable` being
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`fetch_op`'s input. Then we also set `fetch_op` is a target.
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### Algorithm
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If an operator needs to be run, it must fall into one of the following cases:
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1. It is the target.
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2. It is depended by some other ops, meaning its output is some other op's input.
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The first case can be checked by `op_desc.is_traget()` . The second case can be implement as
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```c++
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bool HasDependentVar(const OpDesc& op_desc, const std::set<string>& dependent_vars) {
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for (auto& var : op_desc.outputs()) {
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for (auto& argu : var.arguments()) {
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if (dependent_vars.count(argu) != 0) {
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return true;
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}
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}
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}
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return false;
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}
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```
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Then the whole algorithm can be implemented as the following [code](https://github.com/tonyyang-svail/Paddle/blob/prune_impl/paddle/framework/prune.cc).
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